Using theoretical ROC curves for analysing machine learning binary classifiers

@article{Omar2019UsingTR,
  title={Using theoretical ROC curves for analysing machine learning binary classifiers},
  author={Luma Qassam Abedalqader Omar and Ioannis P. Ivrissimtzis},
  journal={Pattern Recognit. Lett.},
  year={2019},
  volume={128},
  pages={447-451}
}
Most binary classifiers work by processing the input to produce a scalar response and comparing it to a threshold value. The various measures of classifier performance assume, explicitly or implicitly, probability distributions $P_s$ and $P_n$ of the response belonging to either class, probability distributions for the cost of each type of misclassification, and compute a performance score from the expected cost. In machine learning, classifier responses are obtained experimentally and… Expand
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